Everyday around the world, businesses, universities, governments and other organizations need to do complex calculations in the course of scientific research, financial or behavioral modeling, forecasting, risk determination, business planning and other endeavors. Such calculations can consume an enormous amount of computing resources. Historically supercomputers were used for this sort of processing. However, as the demands for this type of processing grow exponentially, “grid” computing is being used as an alternative to expensive supercomputers.
Grid computing, as the term is used herein, simply stated, is a form of distributed computing that allows a large set of processors or engines in multiple geographies to work together as a collection or “grid.” A processor, for example, may be the central processing unit (CPU) in a personal computer, workstation, or server. An “engine” is the processing core of such a computing platform, which in its simplest form may be a single CPU, or may be multiple processors working together in the same platform. Grid distribution software, such as that developed and licensed by Datasynpse, Inc. of New York, N.Y., U.S.A. is applied to farm out the individual calculations, each of which is relatively small, across thousands of processors or engines. Each one performs its calculation and returns the answers to the requesting application for consolidation and reporting.
In some grid computing systems the distribution of work across the grid is managed by entities called “brokers” and data needed by the engines on the grid is housed at and retrieved from data providers, machines which store input data needed for calculations. Thus, at least some modern computing grids include engines (processors or CPU's), brokers and data providers, all distributed across the grid.